Integrating digital interventions with clinical practice in youth mental health services.

OBJECTIVE: Integrating digital technologies with clinical practice promises to improve access and enhance care in the context of high service demand and constrained capacity. METHOD: We outline the emerging research in the integration of digital tools in clinical care, known as blended care, and provide case examples of mental health technology platforms currently in use, summarise findings regarding novel technologies such as virtual reality, and outline real-world implementation challenges and potential solutions. RESULTS: Recent evidence shows that blended care approaches are clinically effective and improve service efficiency. Youth-specific technologies such as moderated online social therapy (MOST) are achieving a range of positive clinical and functional outcomes, while emerging technologies like virtual reality have strong evidence in anxiety disorder, and accumulating evidence in psychotic conditions. Implementation science frameworks show promise in helping overcome the common challenges faced in real-world adoption and ongoing use. CONCLUSION: The integrated, blended use of digital mental health technologies with face-to-face clinical care has the potential to improve care quality for young people while helping overcome the growing challenges faced by youth mental health service providers.

Clinical staging and the differential risks for clinical and functional outcomes in young people presenting for youth mental health care.

BACKGROUND: Clinical staging proposes that youth-onset mental disorders develop progressively, and that active treatment of earlier stages should prevent progression to more severe disorders. This retrospective cohort study examined the longitudinal relationships between clinical stages and multiple clinical and functional outcomes within the first 12 months of care. METHODS: Demographic and clinical information of 2901 young people who accessed mental health care at age 12-25 years was collected at predetermined timepoints (baseline, 3 months, 6 months, 12 months). Initial clinical stage was used to define three fixed groups for analyses (stage 1a: 'non-specific anxious or depressive symptoms', 1b: 'attenuated mood or psychotic syndromes', 2+: 'full-threshold mood or psychotic syndromes'). Logistic regression models, which controlled for age and follow-up time, were used to compare clinical and functional outcomes (role and social function, suicidal ideation, alcohol and substance misuse, physical health comorbidity, circadian disturbances) between staging groups within the initial 12 months of care. RESULTS: Of the entire cohort, 2093 young people aged 12-25 years were followed up at least once over the first 12 months of care, with 60.4% female and a baseline mean age of 18.16 years. Longitudinally, young people at stage 2+ were more likely to develop circadian disturbances (odds ratio [OR]=2.58; CI 1.60-4.17), compared with individuals at stage 1b. Additionally, stage 1b individuals were more likely to become disengaged from education/employment (OR=2.11, CI 1.36-3.28), develop suicidal ideations (OR=1.92; CI 1.30-2.84) and circadian disturbances (OR=1.94, CI 1.31-2.86), compared to stage 1a. By contrast, we found no relationship between clinical stage and the emergence of alcohol or substance misuse and physical comorbidity. CONCLUSIONS: The differential rates of emergence of poor clinical and functional outcomes between early versus late clinical stages support the clinical staging model's assumptions about illness trajectories for mood and psychotic syndromes. The greater risk of progression to poor outcomes in those who present with more severe syndromes may be used to guide specific intervention packages.

Predicting the emergence of full-threshold bipolar I, bipolar II and psychotic disorders in young people presenting to early intervention mental health services.

BACKGROUND: Predictors of new-onset bipolar disorder (BD) or psychotic disorder (PD) have been proposed on the basis of retrospective or prospective studies of 'at-risk' cohorts. Few studies have compared concurrently or longitudinally factors associated with the onset of BD or PDs in youth presenting to early intervention services. We aimed to identify clinical predictors of the onset of full-threshold (FT) BD or PD in this population. METHOD: Multi-state Markov modelling was used to assess the relationships between baseline characteristics and the likelihood of the onset of FT BD or PD in youth (aged 12-30) presenting to mental health services. RESULTS: Of 2330 individuals assessed longitudinally, 4.3% (n = 100) met criteria for new-onset FT BD and 2.2% (n = 51) met criteria for a new-onset FT PD. The emergence of FT BD was associated with older age, lower social and occupational functioning, mania-like experiences (MLE), suicide attempts, reduced incidence of physical illness, childhood-onset depression, and childhood-onset anxiety. The emergence of a PD was associated with older age, male sex, psychosis-like experiences (PLE), suicide attempts, stimulant use, and childhood-onset depression. CONCLUSIONS: Identifying risk factors for the onset of either BD or PDs in young people presenting to early intervention services is assisted not only by the increased focus on MLE and PLE, but also by recognising the predictive significance of poorer social function, childhood-onset anxiety and mood disorders, and suicide attempts prior to the time of entry to services. Secondary prevention may be enhanced by greater attention to those risk factors that are modifiable or shared by both illness trajectories.

Optimising the integration of technology-enabled solutions to enhance primary mental health care: a service mapping study.

BACKGROUND: Despite the widely acknowledged potential for health information technologies to improve the accessibility, quality and clinical safety of mental health care, implementation of such technologies in services is frequently unsuccessful due to varying consumer, health professional, and service-level factors. The objective of this co-design study was to use process mapping (i.e. service mapping) to illustrate the current consumer journey through primary mental health services, identify barriers to and facilitators of quality mental health care, and highlight potential points at which to integrate the technology-enabled solution to optimise the provision of care based on key service performance indicators. METHODS: Interactive, discussion-based workshops of up to six hours were conducted with representative stakeholders from each participating service, including health professionals, service managers and administrators from Open Arms - Veterans & Families Counselling Service (Sydney), a counselling service for veterans and their families, and five headspace centres in the North Coast Primary Health Network, primary youth mental health services. Service maps were drafted and refined in real time during the workshops. Through both group discussion and the use of post-it notes, participants worked together to evaluate performance indicators (e.g. safety) at each point in the consumer journey (e.g. intake) to indicate points of impact for the technology-enabled solution, reviewing and evaluating differing opinions in order to reach consensus. RESULTS: Participants (n=84 across participating services) created service maps illustrating the current consumer journey through the respective services and highlighting barriers to and facilitators of quality mental health care. By consensus, the technology-enabled solution as facilitated by the InnoWell Platform was noted to enable the early identification of risk, reduce or eliminate lengthy intake processes, enable routine outcome monitoring to revise treatment plans in relation to consumer response, and serve as a personal data record for consumers, driving person-centred, coordinated care. CONCLUSIONS: Service mapping was shown to be an effective methodology to understand the consumer's journey through a service and served to highlight how the co-designed technology-enabled solution can optimise service pathways to improve the accessibility, quality and clinical safety of care relative to key service performance indicators, facilitating the delivery of the right care.

Project Synergy: co-designing technology-enabled solutions for Australian mental health services reform.

Project Synergy aims to test the potential of new and emerging technologies to enhance the quality of mental health care provided by traditional face-to-face services. Specifically, it seeks to ensure that consumers get the right care, first time (delivery of effective mental health care early in the course of illness). Using co-design with affected individuals, Project Synergy has built, implemented and evaluated an online platform to assist the assessment, feedback, management and monitoring of people with mental disorders. It also promotes the maintenance of wellbeing by collating health and social information from consumers, their supportive others and health professionals. This information is reported back openly to consumers and their service providers to promote genuine collaborative care. The online platform does not provide stand-alone medical or health advice, risk assessment, clinical diagnosis or treatment; instead, it supports users to decide what may be suitable care options. Using an iterative cycle of research and development, the first four studies of Project Synergy (2014-2016) involved the development of different types of online prototypes for young people (i) attending university; (ii) in three disadvantaged communities in New South Wales; (iii) at risk of suicide; and (iv) attending five headspace centres. These contributed valuable information concerning the co-design, build, user testing and evaluation of prototypes, as well as staff experiences during development and service quality improvements following implementation. Through ongoing research and development (2017-2020), these prototypes underpin one online platform that aims to support better multidimensional mental health outcomes for consumers; more efficient, effective and appropriate use of health professional knowledge and clinical skills; and quality improvements in mental health service delivery.

Right care, first time: a highly personalised and measurement-based care model to manage youth mental health.

Mood and psychotic syndromes most often emerge during adolescence and young adulthood, a period characterised by major physical and social change. Consequently, the effects of adolescent-onset mood and psychotic syndromes can have long term consequences. A key clinical challenge for youth mental health is to develop and test new systems that align with current evidence for comorbid presentations and underlying neurobiology, and are useful for predicting outcomes and guiding decisions regarding the provision of appropriate and effective care. Our highly personalised and measurement-based care model includes three core concepts: ▶ A multidimensional assessment and outcomes framework that includes: social and occupational function; self-harm, suicidal thoughts and behaviour; alcohol or other substance misuse; physical health; and illness trajectory. ▶ Clinical stage. ▶ Three common illness subtypes (psychosis, anxious depression, bipolar spectrum) based on proposed pathophysiological mechanisms (neurodevelopmental, hyperarousal, circadian). The model explicitly aims to prevent progression to more complex and severe forms of illness and is better aligned to contemporary models of the patterns of emergence of psychopathology. Inherent within this highly personalised approach is the incorporation of other evidence-based processes, including real-time measurement-based care as well as utilisation of multidisciplinary teams of health professionals. Data-driven local system modelling and personalised health information technologies provide crucial infrastructure support to these processes for better access to, and higher quality, mental health care for young people. CHAPTER 1: MULTIDIMENSIONAL OUTCOMES IN YOUTH MENTAL HEALTH CARE: WHAT MATTERS AND WHY?: Mood and psychotic syndromes present one of the most serious public health challenges that we face in the 21st century. Factors including prevalence, age of onset, and chronicity contribute to substantial burden and secondary risks such as alcohol or other substance misuse. Mood and psychotic syndromes most often emerge during adolescence and young adulthood, a period characterised by major physical and social change; thus, effects can have long term consequences. We propose five key domains which make up a multidimensional outcomes framework that aims to address the specific needs of young people presenting to health services with emerging mental illness. These include social and occupational function; self-harm, suicidal thoughts and behaviours; alcohol or other substance misuse; physical health; and illness type, stage and trajectory. Impairment and concurrent morbidity are well established in young people by the time they present for mental health care. Despite this, services and health professionals tend to focus on only one aspect of the presentation - illness type, stage and trajectory - and are often at odds with the preferences of young people and their families. There is a need to address the disconnect between mental health, physical health and social services and interventions, to ensure that youth mental health care focuses on the outcomes that matter to young people. CHAPTER 2: COMBINING CLINICAL STAGE AND PATHOPHYSIOLOGICAL MECHANISMS TO UNDERSTAND ILLNESS TRAJECTORIES IN YOUNG PEOPLE WITH EMERGING MOOD AND PSYCHOTIC SYNDROMES: Traditional diagnostic classification systems for mental disorders map poorly onto the early stages of illness experienced by young people, and purport categorical distinctions that are not readily supported by research into genetic, environmental and neurobiological risk factors. Consequently, a key clinical challenge in youth mental health is to develop and test new classification systems that align with current evidence on comorbid presentations, are consistent with current understanding of underlying neurobiology, and provide utility for predicting outcomes and guiding decisions regarding the provision of appropriate and effective care. This chapter outlines a transdiagnostic framework for classifying common adolescent-onset mood and psychotic syndromes, combining two independent but complementary dimensions: clinical staging, and three proposed pathophysiological mechanisms. Clinical staging reflects the progression of mental disorders and is in line with the concept used in general medicine, where more advanced stages are associated with a poorer prognosis and a need for more intensive interventions with a higher risk-to-benefit ratio. The three proposed pathophysiological mechanisms are neurodevelopmental abnormalities, hyperarousal and circadian dysfunction, which, over time, have illness trajectories (or pathways) to psychosis, anxious depression and bipolar spectrum disorders, respectively. The transdiagnostic framework has been evaluated in young people presenting to youth mental health clinics of the University of Sydney's Brain and Mind Centre, alongside a range of clinical and objective measures. Our research to date provides support for this framework, and we are now exploring its application to the development of more personalised models of care. CHAPTER 3: A COMPREHENSIVE ASSESSMENT FRAMEWORK FOR YOUTH MENTAL HEALTH: GUIDING HIGHLY PERSONALISED AND MEASUREMENT-BASED CARE USING MULTIDIMENSIONAL AND OBJECTIVE MEASURES: There is an urgent need for improved care for young people with mental health problems, in particular those with subthreshold mental disorders that are not sufficiently severe to meet traditional diagnostic criteria. New comprehensive assessment frameworks are needed to capture the biopsychosocial profile of a young person to drive highly personalised and measurement-based mental health care. We present a range of multidimensional measures involving five key domains: social and occupational function; self-harm, suicidal thoughts and behaviours; alcohol or other substance misuse; physical health; and illness type, stage and trajectory. Objective measures include: neuropsychological function; sleep-wake behaviours and circadian rhythms; metabolic and immune markers; and brain structure and function. The recommended multidimensional measures facilitate the development of a comprehensive clinical picture. The objective measures help to further develop informative and novel insights into underlying pathophysiological mechanisms and illness trajectories to guide personalised care plans. A panel of specific multidimensional and objective measures are recommended as standard clinical practice, while others are recommended secondarily to provide deeper insights with the aim of revealing alternative clinical paths for targeted interventions and treatments matched to the clinical stage and proposed pathophysiological mechanisms of the young person. CHAPTER 4: PERSONALISING CARE OPTIONS IN YOUTH MENTAL HEALTH: USING MULTIDIMENSIONAL ASSESSMENT, CLINICAL STAGE, PATHOPHYSIOLOGICAL MECHANISMS, AND INDIVIDUAL ILLNESS TRAJECTORIES TO GUIDE TREATMENT SELECTION: New models of mental health care for young people require that interventions be matched to illness type, clinical stage, underlying pathophysiological mechanisms and individual illness trajectories. Narrow syndrome-focused classifications often direct clinical attention away from other key factors such as functional impairment, self-harm and suicidality, alcohol or other substance misuse, and poor physical health. By contrast, we outline a treatment selection guide for early intervention for adolescent-onset mood and psychotic syndromes (ie, active treatments and indicated and more specific secondary prevention strategies). This guide is based on experiences with the Brain and Mind Centre's highly personalised and measurement-based care model to manage youth mental health. The model incorporates three complementary core concepts: ▶A multidimensional assessment and outcomes framework including: social and occupational function; self-harm, suicidal thoughts and behaviours; alcohol or other substance misuse; physical health; and illness trajectory. ▶Clinical stage. ▶Three common illness subtypes (psychosis, anxious depression, bipolar spectrum) based on three underlying pathophysiological mechanisms (neurodevelopmental, hyperarousal, circadian). These core concepts are not mutually exclusive and together may facilitate improved outcomes through a clinical stage-appropriate and transdiagnostic framework that helps guide decisions regarding the provision of appropriate and effective care options. Given its emphasis on adolescent-onset mood and psychotic syndromes, the Brain and Mind Centre's model of care also respects a fundamental developmental perspective - categorising childhood problems (eg, anxiety and neurodevelopmental difficulties) as risk factors and respecting the fact that young people are in a period of major biological and social transition. Based on these factors, a range of social, psychological and pharmacological interventions are recommended, with an emphasis on balancing the personal benefit-to-cost ratio. CHAPTER 5: A SERVICE DELIVERY MODEL TO SUPPORT HIGHLY PERSONALISED AND MEASUREMENT-BASED CARE IN YOUTH MENTAL HEALTH: Over the past decade, we have seen a growing focus on creating mental health service delivery models that better meet the unique needs of young Australians. Recent policy directives from the Australian Government recommend the adoption of stepped-care services to improve the appropriateness of care, determined by severity of need. Here, we propose that a highly personalised approach enhances stepped-care models by incorporating clinical staging and a young person's current and multidimensional needs. It explicitly aims to prevent progression to more complex and severe forms of illness and is better aligned to contemporary models of the patterns of emergence of psychopathology. Inherent within a highly personalised approach is the incorporation of other evidence-based processes, including real-time measurement-based care and use of multidisciplinary teams of health professionals. Data-driven local system modelling and personalised health information technologies provide crucial infrastructure support to these processes for better access to, and higher quality of, mental health care for young people.

The digital cumulative complexity model: a framework for improving engagement in digital mental health interventions.

Mental health disorders affect a substantial portion of the global population. Despite preferences for psychotherapy, access remains limited due to various barriers. Digital mental health interventions (DMHIs) have emerged to increase accessibility, yet engagement and treatment completion rates are concerning. Evidence across healthcare where some degree of self-management is required show that treatment engagement is negatively influenced by contextual complexity. This article examines the non-random factors influencing patient engagement in digital and face-to-face psychological therapies. It reviews established models and introduces an adapted version of the Cumulative Complexity Model (CuCoM) as a framework for understanding engagement in the context of digital mental health. Theoretical models like the Fogg Behavior Model, Persuasive System Design, Self-Determination Theory, and Supportive Accountability aim to explain disengagement. However, none adequately consider these broader contextual factors and their complex interactions with personal characteristics, intervention requirements and technology features. We expand on these models by proposing an application of CuCoM's application in mental health and digital contexts (known as DiCuCoM), focusing on the interplay between patient burden, personal capacity, and treatment demands. Standardized DMHIs often fail to consider individual variations in burden and capacity, leading to engagement variation. DiCuCoM highlights the need for balancing patient workload with capacity to improve engagement. Factors such as life demands, burden of treatment, and personal capacity are examined for their influence on treatment adherence. The article proposes a person-centered approach to treatment, informed by models like CuCoM and Minimally Disruptive Medicine, emphasizing the need for mental healthcare systems to acknowledge and address the unique burdens and capacities of individuals. Strategies for enhancing engagement include assessing personal capacity, reducing treatment burden, and utilizing technology to predict and respond to disengagement. New interventions informed by such models could lead to better engagement and ultimately better outcomes.

An open trial of the feasibility of brief internet-delivered acceptance and aommitment therapy (iACT) for chronic anxiety and depression.

Anxiety and depressive disorders are common, often chronic and result in significant disability and distress. The delivery of psychological interventions via the internet is now recognised to be a safe and effective way to treat these disorders. The predominant therapeutic model in clinical trials and in routine care has been cognitive-behavioural therapy (CBT), which helps patients identify and modify unhelpful thoughts and behaviours. However, other models of treatment for anxiety and depression, such as acceptance and commitment therapy (ACT), which uses the examination of both positive and negative experiences in the service of living a personally meaningful and values-based life, have been developed and tested, although most of these interventions are long and require more clinician support to ensure adherence and achieve positive outcomes. The aim of the present study was to examine the feasibility of a new brief, clinician supported transdiagnostic internet-delivered (iACT) program, designed to treat symptoms of both anxiety and depression and improve social function. A single-group open trial was conducted on 24 adults with long-term symptoms of anxiety and depression. The course is comprised of five online modules delivered over 8 weeks either self-guided or with support from a clinician. There was a high course completion rate (70 %) and a high level of satisfaction with the course (94 % satisfied or very satisfied). Significant clinical improvement in our primary outcome measures (within-group Cohen's d) of anxiety (d ≥ 0.62), depression (d ≥ 0.63), disability (d ≥ 0.43) and quality of life (d ≥ -0.57) were observed at posttreatment. Relatively little clinician time was required per participant (M = 30.6 min, SD = 5.7). The findings of the current study support the feasibility and potential of a transdiagnostic iACT treatment for adults experiencing long-term symptoms of anxiety and depression, including those patients who have not derived benefit from other treatments.

Digital Application of Clinical Staging to Support Stratification in Youth Mental Health Services: Validity and Reliability Study.

BACKGROUND: As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to individuals' risk of illness progression. The application of staging has been traditionally limited to trained clinicians yet leveraging digital technologies to apply clinical staging could increase the scalability and usability of this model in services. OBJECTIVE: The aim of this study was to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders. METHODS: We conducted a study with a cohort comprising 131 young people, aged between 16 and 25 years, who presented to youth mental health services in Australia between November 2018 and March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm's allocation based on a multidimensional self-report questionnaire. RESULTS: Of the 131 participants, the mean age was 20.3 (SD 2.4) years, and 72% (94/131) of them were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the experts' ratings, with a substantial interrater agreement (κ=0.67; P<.001). The algorithm demonstrated an accuracy of 91% (95% CI 86%-95%; P=.03), a sensitivity of 80%, a specificity of 93%, and an F1-score of 73%. Of the concordant ratings, 16 young people were allocated to stage 1a, while 103 were assigned to stage 1b+. Among the 12 discordant cases, the digital algorithm allocated a lower stage (stage 1a) to 8 participants compared to the experts. These individuals had significantly milder symptoms of depression (P<.001) and anxiety (P<.001) compared to those with concordant stage 1b+ ratings. CONCLUSIONS: This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in the early stages of common anxiety and depressive disorders. Between 11% and 27% of young people seeking care may benefit from low-intensity, self-directed, or brief interventions. Findings from this study suggest the possibility of redirecting clinical capacity to focus on individuals in stage 1b+ for further assessment and intervention.

Predictors of functional impairment at assessment and functional improvement after treatment at a national digital mental health service.

Mental disorders are associated with impairment to daily functioning, which affects both the individual and society. Despite this, most research on treatment outcome only report symptom change. Self-reported days out of role (DOR) is a simple measure of functional impairment used in many population studies. The current study sought to report on the degree of functional impairment measured by DOR in a clinical sample at assessment, the factors associated with this impairment, the predictors of functional improvement after treatment and the relationship between symptomatic and functional change. Using a prospective uncontrolled observational cohort study design with a sample of 17,813 patients accessing a digital mental health service (DMHS), we examined self-reported demographic, psychosocial and clinical data. Using a series of univariate regression models and multivariate classification algorithms, we found that baseline DOR was associated with age, employment and relationship status, symptom severity, symptom chronicity and with the presence of several psychosocial difficulties. Baseline DOR was best predicted by older age, disability payments, higher symptom severity and increasing number of endorsed psychosocial difficulties (R2 = 32.7 %). Forty-one per cent of the sample experienced a >50 % or greater reduction in DOR following treatment. Those who were separated, unemployed or on disability payments, or with severe and chronic depression, experienced the greatest reductions in DOR after treatment. Changes in functioning were independent of changes in symptoms, highlighting the importance of functional impairment as a treatment outcome. This study found that many of the patients who access DMHS have significant levels of functional impairment, a large proportion obtain functional improvement after treatment, and improvement in function after treatment was independent of improvement in symptoms.

Factors associated with treatment uptake, completion, and subsequent symptom improvement in a national digital mental health service.

Digital mental health services (DMHS) have proven effectiveness and play an important role within the broader mental health system by reducing barriers to evidence-based care. However, improved understanding of the factors associated with successful treatment uptake, treatment completion and positive clinical outcomes will facilitate efforts to maximise outcomes. Previous studies have demonstrated that patient age is positively associated, and initial symptom severity negatively associated with treatment uptake and treatment completion rates in both DMHS and other mental health services. The current study sought to extend these findings by examining the effect of other patient characteristics, in particular, self-reported psychosocial difficulties, using data from a large-scale national DMHS. Using a prospective uncontrolled observational cohort study design, we collected self-reported demographic, psychosocial and clinical data from 15,882 patients who accessed the MindSpot Clinic, Australia, between 1 January and 31 December 2019. Using a series of univariate regression models and multivariate classification algorithms we found that older age, higher educational attainment, and being in a relationship were all positively associated with uptake, completion and significant symptom improvement, while higher initial symptom severity was negatively associated with those outcomes. In addition, self-reported psychosocial difficulties had a significant negative impact on uptake, completion, and symptom improvement. Consistent with previous literature, the presence of these characteristics in isolation or in combination have a significant impact on treatment uptake, completion, and symptomatic improvement. Individual and multiple psychosocial difficulties are associated with reduced capacity to participate in treatment and hence an increased treatment burden. Identifying patients with lower capacity to complete treatment, modifications to treatments and the provision of supports to reduce treatment burden may promote greater engagement and completion of treatments offered by digital mental health services.

Right Care, First Time: Developing a Theory-Based Automated Protocol to Help Clinically Stage Young People Based on Severity and Persistence of Mental Illness.

Most mental disorders emerge before the age of 25 years and, if left untreated, have the potential to lead to considerable lifetime burden of disease. Many services struggle to manage high demand and have difficulty matching individuals to timely interventions due to the heterogeneity of disorders. The technological implementation of clinical staging for youth mental health may assist the early detection and treatment of mental disorders. We describe the development of a theory-based automated protocol to facilitate the initial clinical staging process, its intended use, and strategies for protocol validation and refinement. The automated clinical staging protocol leverages the clinical validation and evidence base of the staging model to improve its standardization, scalability, and utility by deploying it using Health Information Technologies (HIT). Its use has the potential to enhance clinical decision-making and transform existing care pathways, but further validation and evaluation of the tool in real-world settings is needed.

Supporting Clinicians to Use Technology to Deliver Highly Personalized and Measurement-Based Mental Health Care to Young People: Protocol for an Evaluation Study.

BACKGROUND: Australia's mental health care system has long been fragmented and under-resourced, with services falling well short of demand. In response, the World Economic Forum has recently called for the rapid deployment of smarter, digitally enhanced health services to facilitate effective care coordination and address issues of demand. The University of Sydney's Brain and Mind Centre (BMC) has developed an innovative digital health solution that incorporates 2 components: a highly personalized and measurement-based (data-driven) model of youth mental health care and a health information technology (HIT) registered on the Australian Register of Therapeutic Goods. Importantly, research into implementation of such solutions considers education and training of clinicians to be essential to adoption and optimization of use in standard clinical practice. The BMC's Youth Mental Health and Technology Program has subsequently developed a comprehensive education and training program to accompany implementation of the digital health solution. OBJECTIVE: This paper describes the protocol for an evaluation study to assess the effectiveness of the education and training program on the adoption and optimization of use of the digital health solution in service delivery. It also describes the proposed tools to assess the impact of training on knowledge and skills of mental health clinicians. METHODS: The evaluation study will use the Kirkpatrick Evaluation Model as a framework with 4 levels of analysis: Reaction (to education and training), Learning (knowledge acquired), Behavior (practice change), and Results (client outcomes). Quantitative and qualitative data will be collected using a variety of tools, including evaluation forms, pre- and postknowledge questionnaires, skill development and behavior change scales, as well as a real-time clinical practice audit. RESULTS: This project is funded by philanthropic funding from Future Generation Global. Ethics approval has been granted via Sydney Local Health District's Human Research Ethics Committee. At the time of this publication, clinicians and their services were being recruited to this study. The first results are expected to be submitted for publication in 2021. CONCLUSIONS: The education and training program teaches clinicians the necessary knowledge and skills to assess, monitor, and manage complex needs; mood and psychotic syndromes; and trajectories of youth mental ill-health using a HIT that facilitates a highly personalized and measurement-based model of care. The digital health solution may therefore guide clinicians to help young people recover low functioning associated with subthreshold diagnostic presentations and prevent progression to more serious mental ill-health. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/24697.

Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study.

BACKGROUND: A priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this does not preclude the utility of prediction models to improve decision-making regarding a service response in terms of more detailed assessments and/or intervention. The aim of this study was to predict self-harm within six-months after initial presentation. METHOD: The study included 1962 young people (12-30 years) presenting to youth mental health services in Australia. Six machine learning algorithms were trained and tested with ten repeats of ten-fold cross-validation. The net benefit of these models were evaluated using decision curve analysis. RESULTS: Out of 1962 young people, 320 (16%) engaged in self-harm in the six months after first assessment and 1642 (84%) did not. The top 25% of young people as ranked by mean predicted probability accounted for 51.6% - 56.2% of all who engaged in self-harm. By the top 50%, this increased to 82.1%-84.4%. Models demonstrated fair overall prediction (AUROCs; 0.744-0.755) and calibration which indicates that predicted probabilities were close to the true probabilities (brier scores; 0.185-0.196). The net benefit of these models were positive and superior to the 'treat everyone' strategy. The strongest predictors were (in ranked order); a history of self-harm, age, social and occupational functioning, sex, bipolar disorder, psychosis-like experiences, treatment with antipsychotics, and a history of suicide ideation. CONCLUSION: Prediction models for self-harm may have utility to identify a large sub population who would benefit from further assessment and targeted (low intensity) interventions. Such models could enhance health service approaches to identify and reduce self-harm, a considerable source of distress, morbidity, ongoing health care utilisation and mortality.

Transdiagnostic clinical staging in youth mental health: a first international consensus statement.

Recognizing that current frameworks for classification and treatment in psychiatry are inadequate, particularly for use in young people and early intervention services, transdiagnostic clinical staging models have gained prominence. These models aim to identify where individuals lie along a continuum of illness, to improve treatment selection and to better understand patterns of illness continuity, discontinuity and aetiopathogenesis. All of these factors are particularly relevant to help-seeking and mental health needs experienced during the peak age range of onset, namely the adolescent and young adult developmental periods (i.e., ages 12-25 years). To date, progressive stages in transdiagnostic models have typically been defined by traditional symptom sets that distinguish "sub-threshold" from "threshold-level" disorders, even though both require clinical assessment and potential interventions. Here, we argue that staging models must go beyond illness progression to capture additional dimensions of illness extension as evidenced by emergence of mental or physical comorbidity/complexity or a marked change in a linked biological construct. To develop further consensus in this nascent field, we articulate principles and assumptions underpinning transdiagnostic clinical staging in youth mental health, how these models can be operationalized, and the implications of these arguments for research and development of new service systems. We then propose an agenda for the coming decade, including knowledge gaps, the need for multi-stakeholder input, and a collaborative international process for advancing both science and implementation.

Youth Mental Health Tracker: protocol to establish a longitudinal cohort and research database for young people attending Australian mental health services.

INTRODUCTION: Mental disorders are a leading cause of long-term disability worldwide. Much of the burden of mental ill-health is mediated by early onset, comorbidities with physical health conditions and chronicity of the illnesses. This study aims to track the early period of mental disorders among young people presenting to Australian mental health services to facilitate more streamlined transdiagnostic processes, highly personalised and measurement-based care, secondary prevention and enhanced long-term outcomes. METHODS AND ANALYSIS: Recruitment to this large-scale, multisite, prospective, transdiagnostic, longitudinal clinical cohort study ('Youth Mental Health Tracker') will be offered to all young people between the ages of 12 and 30 years presenting to participating services with proficiency in English and no history of intellectual disability. Young people will be tracked over 3 years with standardised assessments at baseline and 3, 6, 12, 24 and 36 months. Assessments will include self-report and clinician-administered measures, covering five key domains including: (1) social and occupational function; (2) self-harm, suicidal thoughts and behaviour; (3) alcohol or other substance misuse; (4) physical health; and (5) illness type, clinical stage and trajectory. Data collection will be facilitated by the use of health information technology. The data will be used to: (1) determine prospectively the course of multidimensional functional outcomes, based on the differential impact of demographics, medication, psychological interventions and other key potentially modifiable moderator variables and (2) map pathophysiological mechanisms and clinical illness trajectories to determine transition rates of young people to more severe illness forms. ETHICS AND DISSEMINATION: The study has been reviewed and approved by the Human Research Ethics Committee of the Sydney Local Health District (2019/ETH00469). All data will be non-identifiable, and research findings will be disseminated through peer-reviewed journals and scientific conference presentations.

A Digital Platform Designed for Youth Mental Health Services to Deliver Personalized and Measurement-Based Care.

Mental disorders that commonly emerge during adolescence and young adulthood are associated with substantial immediate burden and risks, as well as potentially imparting lifetime morbidity and premature mortality. While the development of health services that are youth focused and prioritize early intervention has been a critical step forward, an ongoing challenge is the heterogeneous nature of symptom profiles and illness trajectories. Consequently, it is often difficult to provide quality mental health care, at scale, that addresses the broad range of health, social, and functional needs of young people. Here, we describe a new digital platform designed to deliver personalized and measurement-based care. It provides health services and clinicians with the tools to directly address the multidimensional needs of young people. The term "personalized" describes the notion that the assessment of, and the sequence of interventions for, mental disorders are tailored to the young person-and their changing needs over time, while "measurement-based" describes the use of systematic and continuing assessment of a young person's outcomes over the entire course of clinical care. Together, these concepts support a framework for care that transcends a narrow focus on symptom reduction or risk reduction. Instead, it prioritizes a broader focus on enhancing social, health, and physical outcomes for young people and a commitment to tracking these outcomes throughout this key developmental period. Now, with twenty-first century technologies, it is possible to provide health services with the tools needed to deliver quality mental health care.

What is the prevalence, and what are the clinical correlates, of insulin resistance in young people presenting for mental health care? A cross-sectional study.

OBJECTIVES: To report the distribution and predictors of insulin resistance (IR) in young people presenting to primary care-based mental health services. DESIGN: Cross-sectional. SETTING: Headspace-linked clinics operated by the Brain and Mind Centre of the University of Sydney. PARTICIPANTS: 768 young people (66% female, mean age 19.7±3.5, range 12-30 years). MAIN OUTCOME MEASURES: IR was estimated using the updated homeostatic model assessment (HOMA2-IR). Height and weight were collected from direct measurement or self-report for body mass index (BMI). RESULTS: For BMI, 20.6% of the cohort were overweight and 10.2% were obese. However, <1% had an abnormally high fasting blood glucose (>6.9 mmol/L). By contrast, 9.9% had a HOMA2-IR score >2.0 (suggesting development of IR) and 11.7% (n=90) had a score between 1.5 and 2. Further, there was a positive correlation between BMI and HOMA2-IR (r=0.44, p<0.001). Participants in the upper third of HOMA2-IR scores are characterised by younger age, higher BMIs and depression as a primary diagnosis. HOMA2-IR was predicted by younger age (ß=0.19, p<0.001) and higher BMI (ß=0.49, p<0.001), together explaining 22% of the variance (F(2,361)=52.1, p<0.001). CONCLUSIONS: Emerging IR is evident in a significant subgroup of young people presenting to primary care-based mental health services. While the major modifiable risk factor is BMI, a large proportion of the variance is not accounted for by other demographic, clinical or treatment factors. Given the early emergence of IR, secondary prevention interventions may need to commence prior to the development of full-threshold or major mood or psychotic disorders.

Clinical Stage Transitions in Persons Aged 12 to 25 Years Presenting to Early Intervention Mental Health Services With Anxiety, Mood, and Psychotic Disorders.

Importance: The large contribution of psychiatric disorders to premature death and persistent disability among young people means that earlier identification and enhanced long-term care for those who are most at risk of developing life-threatening or chronic disorders is critical. Clinical staging as an adjunct to diagnosis to address emerging psychiatric disorders has been proposed for young people presenting for care; however, the longer-term utility of this system has not been established. Objectives: To determine the rates of transition from earlier to later stages of anxiety, mood, psychotic, or comorbid disorders and to identify the demographic and clinical characteristics that are associated with the time course of these transitions. Design, Setting, and Participants: A longitudinal, observational study of 2254 persons aged 12 to 25 years who obtained mental health care at 2 early intervention mental health services in Sydney, Australia, and were recruited to a research register between June 18, 2008, and July 24, 2018 (the Brain and Mind Centre Optymise Cohort). Main Outcomes and Measures: The primary outcome of this study was transition from earlier to later clinical stages. A multistate Markov model was used to examine demographic (ie, age, sex, engagement in education, employment, or both) and clinical (ie, social and occupational function, clinical presentation, personal history of mental illness, physical health comorbidities, treatment use, self-harm, suicidal thoughts and behaviors) factors associated with these transitions. Results: Of the 2254 individuals included in the study, mean (SD) age at baseline was 18.18 (3.33) years and 1330 (59.0%) were female. Data on race/ethnicity were not available. Median (interquartile range) follow-up was 14 (5-33) months. Of 685 participants at stage 1a (nonspecific symptoms), 253 (36.9%) transitioned to stage 1b (attenuated syndromes). Transition was associated with lower social functioning (hazard ratio [HR], 0.77; 95% CI, 0.66-0.90), engagement with education, employment, or both (HR, 0.47; 95% CI, 0.25-0.91), manic-like experiences (HR, 2.12; 95% CI, 1.19-3.78), psychotic-like experiences (HR, 2.13; 95% CI, 1.38-3.28), self-harm (HR, 1.42; 95% CI, 1.01-1.99), and older age (HR, 1.27; 95% CI, 1.11-1.45). Of 1370 stage 1b participants, 176 (12.8%) transitioned to stage 2 (full-threshold) disorders. Transition was associated with psychotic-like experiences (HR, 2.31; 95% CI, 1.65-3.23), circadian disturbance (HR, 1.66; 95% CI, 1.17-2.35), psychiatric medication (HR, 1.43; 95% CI, 1.03-1.99), childhood psychiatric disorder (HR, 1.62; 95% CI, 1.03-2.54), and older age (HR, 1.24; 95% CI, 1.05-1.45). Conclusions and Relevance: Differential rates of progression from earlier to later stages of anxiety, mood, psychotic, or comorbid disorders were observed in young persons who presented for care at various stages. Understanding the rate and factors associated with transition assists planning of stage-specific clinical interventions and secondary prevention trials.

Variability in Clinical Outcomes for Youths Treated for Subthreshold Severe Mental Disorders at an Early Intervention Service.

OBJECTIVE: The study assessed the longitudinal clinical and functional outcomes of young people receiving early intervention services for subthreshold manifestations of severe mental disorders at a youth mental health service in Sydney, Australia. METHODS: The six-month, prospective, longitudinal study collected data on clinical outcomes of 243 young people treated for subsyndromal presentations of putative major mental disorders. Reliable change index scores and effect sizes were calculated to compare results at baseline, three months, and six months for measures of symptoms (Brief Psychiatric Rating Scale [BPRS]), psychological distress (Kessler 10 [K10]), and social functioning (Social and Occupational Functioning Assessment Scale [SOFAS]). RESULTS: There was variability in outcomes across each of the outcome measures, with 25%, 33%, and 23% of patients showing reliable improvement and 9%, 13%, and 5% showing reliable deterioration for the SOFAS, K10, and BPRS, respectively. Many individuals did not show linear improvement or deterioration, with reliable change within individuals varying significantly between the zero- to three-month and three- to six-month time points. After the analyses were controlled for covariates, baseline severity or impairment and number of sessions with a psychiatrist or psychologist contributed a small (4%-8%) but significant amount to the total variance in outcomes at six months. CONCLUSIONS: Most individuals did not show significant deterioration, and about one in four showed reliable improvement by six months. However, individual patterns of change were diverse, highlighting the importance of further research into factors that influence treatment outcomes among youths with subthreshold presentations of severe mental disorders.